Unsupervised cross-domain image-to-image translation is a very active topic in computer vision and graphics. This task has two challenges: 1) lack of paired training data and 2) numerous possible outputs from a single image. The existing methods rely on either paired data or perform one-to-one translation. A novel Multi-Style Unsupervised image synthesis model using Generative Adversarial Nets (MSU-GAN) is proposed in this paper to overcome these disadvantages. Firstly, the encoder-decoder structure is used to map the image to domain-shared content features space and domain-specific style features space. Secondly, to translate an image into another domain, the content code and the style code are combined to synthesize the resulting image. Finally, the bidirectional cycle-consistency loss is used for the unpaired training data; the inter-domain adversarial loss and the reconstruction loss are used to ensure the output image’s realism. Simultaneously, MSU-GAN is able to synthesize multi-style images due to disentangled representation. A Multi-Style Unsupervised Feature-Wise image synthesis model using Generative Adversarial Nets (MSU-FW-GAN) based on the MSU-GAN is proposed for the shape variation tasks. There are two different testing strategies, which include random style transfer and style guide transfer. For objective comparison, the proposed model performs well on all evaluation metrics. The random style transfer experiment results show that compared with CycleGAN on the photo2portraits dataset, MSU-FW-GAN FID, IS scores dropped by 12.77% and 8.06%. For the summer2winter dataset, MSU-GAN FID and IS scores increased by 24.51% and 3.64%. Qualitative results show that without paired training data, MSU-GAN and MSU-FW-GAN can synthesize multi-style and better realistic images on various tasks.

中文翻译：

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使用生成对抗网络的多风格无监督图像合成

无监督的跨域图像到图像转换是计算机视觉和图形领域中一个非常活跃的话题。这个任务有两个挑战：1）缺乏成对的训练数据和 2）来自单个图像的大量可能的输出。现有方法依赖于配对数据或执行一对一翻译。为了克服这些缺点，本文提出了一种使用生成对抗网络 (MSU-GAN) 的新型多风格无监督图像合成模型。首先，编码器-解码器结构用于将图像映射到领域共享内容特征空间和领域特定风格特征空间。其次，为了将图像转换到另一个域，内容代码和样式代码被组合以合成结果图像。最后，双向循环一致性损失用于未配对的训练数据；域间对抗性损失和重建损失用于确保输出图像的真实性。同时，MSU-GAN 能够通过解开表示合成多风格图像。针对形状变化任务，提出了一种基于 MSU-GAN 的使用生成对抗网络 (MSU-FW-GAN) 的多风格无监督 Feature-Wise 图像合成模型。有两种不同的测试策略，包括随机风格迁移和风格指南迁移。为了客观比较，所提出的模型在所有评估指标上都表现良好。随机风格迁移实验结果表明，在 photo2portraits 数据集 MSU-FW-GAN FID 上，与 CycleGAN 相比，IS 得分下降了 12.77% 和 8.06%。对于 Summer2winter 数据集，MSU-GAN FID 和 IS 得分分别增加了 24.51% 和 3.64%。